You can get really good results with Chinese models. You're putting Opus and GPT on too high of a pedestal.

I use Chinese models (for simple personal projects), they just don't compare to GPT or Opus for any serious work.

I do not know why every Chinese model fan thinks that people that aren't impressed by them simply don't use them.

Wast majority of software engineers do very little except of moving JSONs around and building CRUDs.

It's quite obvious that when you dont try to do something particularly complex there will be literally no difference between GPT, Claude, Gemini and Deepseek.

Fot many things I'm doing in gamedev Gemini 2.5 Pro was already good enough even though it released more than year ago.

Once you pass certain threshold it's just enough.

What constitutes serious work and how seriously have you tried to do serious work with them? While those trying to claim a 30B dense model can match Opus 4.6 are engaging in either beyond over-excessive over-exaggeration or performing rather routine tasks, it's disingenuous in the other direction to claim the latest open 1T models are not useful for serious work. I find those making such claims have rarely spent more than a few minutes on halfhearted attempts and often on recently obsoleted models.

Openweight models turned a corner around kimi 2.6, deepseek v4 pro/flash, hy3 and mimo 2.5 pro. Similar to how closed LLMs turned a corner around gpt 5.2 and opus 4.5.

While they remain a step behind closed frontier models, for real world tasks ranging across functional reactive programming, distributed systems, mathematical modeling, to-the-millisecond highly optimized spatial data-structures, complex compute shaders and shader effects and non-trivial systems involving parser combinators and algebraic effect systems, I can say that open models have very recently gone from useless to productive. For my work, mimo v2.5 pro is hands down better than sonnet 4.6.

I thought these TPUs were primarily used for inference?

TPU8t is for training. But even still, once you’ve trained, you need to run the model too. And these kinds of models already have a huge latency hit so there’s not much hurting running it away from the trading switches.

As the article states, there's both training and inference dedicated chips.

Just interesting to see how SO is evolving.

Do we really need to wonder? it's probably cloudflare.

I shudder when I read DRM after building a embedded display raspberry pi build. There was a problem with Raspberry pi kernel incorrectly detected an HDMI display was connected even though I was trying to display to DSI. The only work around I had was to write a kernel module that would disable HDMI as a possible interface.

The problem is how bullshit transactional emails are when you're outside of AWS. If you're not expecting to use 10,000 emails a month but would like the option to go over the free tier without committing to 10,000 more. Just let me pay per use FFS.

The reason AWS does that is because there is a lot of base level work to verify you as "not a spammer" and to keep verifying you. So this is their way of making sure you pay the base cost.

They could price per use, but it would have to start with a base fee that is about the same at 10,000 emails.

The base fee doesn't need to be monthly, they can take $50 dollars as a one time registration verification fee. That should be enough to cover their compute costs for the year, specially when you pool in that from multiple customers. Who is spamming with 100 monthly emails? How much compute do they need to verify you aren't spamming. They can bake all of that into the pay per use price, they choose not to and I'm glad cloudflare is offering this.

Maintaining the reputation of an IP address is the issue here. If one bad actor sends just a few emails that get marked as spam, the entire IP gets marked as bad. That's basically what you're paying for.

Also, the person who just wants to send a few 100 emails a month is actually far more likely to be a spammer. So it's also a way for them to eliminate those folks.

And lastly, the support burden can be high.

AWS has basically said they only want serious customers, let the other guys worry about the small senders.

Scaleway TEM offer pay per use. I moved off of resend when my side project went beyond their free tier and now pay <0.5€ a month

I agree, software (software startups) has always been the golden child of investors because of how cheap it is compared to hardware or any other physical good.

Good software is expensive regardless of the involvement of LLMs because you need someone to take responsibility. Large companies will save a buck because there may be fewer people needed to take said responsibility, but it's probably a marginal saving compared to the overall scheme of things.

I literally said this exact thing verbatim with someone who was worshipping ai for code. This is an insane idea, really cool.

One bit of feedback, I'd love if this could be a little more aesthetic in the visualization. The method names are a little hard to read, white on light green.

It was just the first look...My primary focus on was on the Engine. I will upgrade the UI and will surely change the UI you mentioned. You can also contribute if you wish as it is open source (built in nextjs)